The Interaction between Objectives and Constraints in Evolutionary Structural Engineering Optimisation

@InProceedings{fenton:2013:USNCCM12,
author = "Michael Fenton and Ciaran McNally and
Michael O'Neill",
title = "The Interaction between Objectives and Constraints in
Evolutionary Structural Engineering Optimisation",
booktitle = "12th U.S. National Congress on Computational Mechanics
(USNCCM12)",
year = "2013",
editor = "John Dolbow and Murthy Guddati",
address = "Raleigh, North Carolina, USA",
month = "22-25 " # jul,
organization = "U.S. Association for Computational Mechanics",
keywords = "genetic algorithms, genetic programming",
abstract = "Selection of appropriate techniques for handling
different constraints is a key part of evolutionary
optimisation in all disciplines. This also applies to
the field of Evolutionary Structural Engineering
Optimisation where multiple conflicting constraints are
present. These constraints include standard engineering
parameters such as stress, strain, deflection,
buckling, and weight; they can however also include
more complex constraints such as an accurate estimate
of the cost of the structure or a subjective assessment
of the architectural form. The selection of appropriate
functions for these constraints, and the subsequent
management of these parameters is a crucial part of the
evolutionary process. Structural engineering
optimisation will often require the designer to satisfy
multiple parallel objectives, and there may be overlaps
between both constraints and objectives. Understanding
the interaction between these constraints and the
overall individual fitness will therefore have a
significant impact on the quality of the designs
produced. As such, a key challenge for designers when
using evolutionary approaches is to find an accurate
metric that will allow the designer to: a) judge
individual constraints, and b) transform the
performance of the individual (relative to those
constraints) into a single coherent value for use by
the fitness function. The effect of differing
constraints on the overall population evolution is
noteworthy. It is shown that the addition of more
constraints does not necessarily reduce the search
space or improve the final population, but can help to
guide the search process where the search space is very
large. The differences between varying degrees of hard
and soft constraints are discussed, as are the
implications of their use in different scenarios. The
most appropriate methods of applying a costing
constraint to a structure are discussed, and
recommendations are made for which method to use.
Finally, the merits of both single-objective and
multiple-objective optimisation for evolutionary
structural engineering optimisation are compared and
contrasted.",
notes = "USNCCM12 is co-hosted by Duke University and North
Carolina State University. Other participating
institutions include Khalifa University of Science
Technology and Research (KUSTAR) and Army Research
Office, Statistical and Applied Mathematical Sciences
Institute (SAMSI). http://12.usnccm.org/",
}